获取列名，该列名在python pandas的任何行中都包含特定值

问题描述

我想基于整个熊猫的特定列中包含的特定值，从整个数据库中获取列名(假设数据库包含100多个行，其中50列以上).

I want to get column name from the whole database (assume the database contains more than 100 rows with more than 50 column) based on specific value that contain in a specific column in pandas.

在Bkmm3(来自印度的成员)的帮助下，我在数字术语上取得了成功，但在字母术语上却失败了.我尝试过的方式是这样的:

with the help of Bkmm3 (member from india) I've succeeded on numerical term but failed on alphabetic term. the way I've tried is this:

df = pd.DataFrame({'A':['APPLE','BALL','CAT'],
                    'B':['ACTION','BATMAN','CATCHUP'],
                    'C':['ADVERTISE','BEAST','CARTOON']})
response = input("input")
for i in df.columns: if(len(df.query(i + '==' + str(response))) > 0):
print(i)`

然后输出出现错误:

Traceback (most recent call last): NameError: name 'APPLE' is not defined

非常感谢你们的帮助，谢谢. .

Any Help from You Guys will be very Appreciated, Thank You . . .

推荐答案

isin/eq适用于DataFrames，您可以将其100％向量化:

isin/eq works for DataFrames, and you can 100% vectorize this:

df.columns[df.isin(['APPLE']).any()]  # df.isin([response])

或者，

df.columns[df.eq(response).any()]

Index(['A'], dtype='object')

这是DataFrame.eval和np.logical_or的回旋方式(要在列上循环):

And here's the roundabout way with DataFrame.eval and np.logical_or (were you to loop on columns):

df.columns[
    np.logical_or.reduce(
        [df.eval(f"{repr(response)} in {i}") for i in df]
)]
Index(['A'], dtype='object')

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